CRF-based Sequence Segmentation Function
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A CRF-based Sequence Segmentation Function is a Sequence Segmentation Function that represents a Conditional Random Field.
- AKA: CRF-based Segmenter.
- Context:
- It can be a CRF-based Sequence Tagging Function that is used as a Sequence Segmentation Function.
- CRFs are naturally Sequence Tagging Models not Sequence Segmenting Models. (Sarawagi & Cohen, 2004)
- It can be a CRF-based Sequence Tagging Function that is used as a Sequence Segmentation Function.
- See: CRF System.
References
2006
- (Sarawagi, 2006) ⇒ Sunita Sarawagi. (2006). “Efficient Inference on Sequence Segmentation Models.” In: Proceedings of the 23rd International Conference on Machine Learning (ICML 2006). doi:10.1145/1143844.1143944
- Traditionally many of these applications have been artificially formulated as sequence labeling tasks at the expense of a loss of flexibility of features that can be exploited.
2005
- (Wallach, 2005) ⇒ Hanna M. Wallach. (2005). “Conditional Random Fields." Literature Survey Webpage.
- Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices.
2004
- (Sarawagi & Cohen, 2004) ⇒ Sunita Sarawagi, and William W. Cohen. (2004). “Semi-Markov Conditional Random Fields for Information Extraction.” In: Proceedings of Advances in Neural Information Processing Systems, 17 NIPS 2004.